Detecting spam documents in a phrase based information retrieval system

ABSTRACT

An information retrieval system uses phrases to index, retrieve, organize and describe documents. Phrases are identified that predict the presence of other phrases in documents. Documents are the indexed according to their included phrases. A spam document is identified based on the number of related phrases included in a document.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of application Ser. No.10/900,021, filed on Jul. 26, 2004, which is co-owned, and incorporatedby reference herein.

FIELD OF THE INVENTION

The present invention relates to an information retrieval system forindexing, searching, and classifying documents in a large scale corpus,such as the Internet.

BACKGROUND OF THE INVENTION

Information retrieval systems, generally called search engines, are nowan essential tool for finding information in large scale, diverse, andgrowing corpuses such as the Internet. Generally, search engines createan index that relates documents (or “pages”) to the individual wordspresent in each document. A document is retrieved in response to a querycontaining a number of query terms, typically based on having somenumber of query terms present in the document. The retrieved documentsare then ranked according to other statistical measures, such asfrequency of occurrence of the query terms, host domain, link analysis,and the like. The retrieved documents are then presented to the user,typically in their ranked order, and without any further grouping orimposed hierarchy. In some cases, a selected portion of a text of adocument is presented to provide the user with a glimpse of thedocument's content.

Direct “Boolean” matching of query terms has well known limitations, andin particular does not identify documents that do not have the queryterms, but have related words. For example, in a typical Boolean system,a search on “Australian Shepherds” would not return documents aboutother herding dogs such as Border Collies that do not have the exactquery terms. Rather, such a system is likely to also retrieve and highlyrank documents that are about Australia (and have nothing to do withdogs), and documents about “shepherds” generally.

The problem here is that conventional systems index documents based onindividual terms, rather than on concepts. Concepts are often expressedin phrases, such as “Australian Shepherd,” “President of the UnitedStates,” or “Sundance Film Festival”. At best, some prior systems willindex documents with respect to a predetermined and very limited set of‘known’ phrases, which are typically selected by a human operator.Indexing of phrases is typically avoided because of the perceivedcomputational and memory requirements to identify all possible phrasesof say three, four, or five or more words. For example, on theassumption that any five words could constitute a phrase, and a largecorpus would have at least 200,000 unique terms, there wouldapproximately 3.2×10²⁶ possible phrases, clearly more than any existingsystem could store in memory or otherwise programmatically manipulate. Afurther problem is that phrases continually enter and leave the lexiconin terms of their usage, much more frequently than new individual wordsare invented. New phrases are always being generated, from sources suchtechnology, arts, world events, and law. Other phrases will decline inusage over time.

Another problem that arises in existing information retrieval systems isthe appearance of “spam” documents. Some spam pages are documents thathave little if any meaningful content, but instead comprise collectionsof popular words and phrases, often hundreds or even thousands of them;these pages are sometime called “keyword stuffing pages.” Others includespecific words and phrases known to be of interest to advertisers. Thesetypes of documents (often called “honeypots”) are created to causesearch engines to retrieve such documents for display along with paidadvertisements. However, to the user searching for meaningful content,retrieval of such documents results in waste of time, and frustration.

Accordingly, there is a need for an information retrieval system andmethodology that can comprehensively identify phrases in a large scalecorpus, index documents according to phrases. In addition, there is aneed in such an information retrieval system to identify spam documentsand filter such documents from search results.

SUMMARY OF THE INVENTION

An information retrieval system and methodology uses phrases to index,search, rank, and describe documents in the document collection. Thesystem is adapted to identify phrases that have sufficiently frequentand/or distinguished usage in the document collection to indicate thatthey are “valid” or “good” phrases. In this manner multiple wordphrases, for example phrases of four, five, or more terms, can beidentified. This avoids the problem of having to identify and indexevery possible phrases resulting from the all of the possible sequencesof a given number of words.

The system is further adapted to identify phrases that are related toeach other, based on a phrase's ability to predict the presence of otherphrases in a document. More specifically, a prediction measure is usedthat relates the actual co-occurrence rate of two phrases to an expectedco-occurrence rate of the two phrases. Information gain, as the ratio ofactual co-occurrence rate to expected co-occurrence rate, is one suchprediction measure. Two phrases are related where the prediction measureexceeds a predetermined threshold. In that case, the second phrase hassignificant information gain with respect to the first phrase.Semantically, related phrases will be those that are commonly used todiscuss or describe a given topic or concept, such as “President of theUnited States” and “White House.” For a given phrase, the relatedphrases can be ordered according to their relevance or significancebased on their respective prediction measures.

The information retrieval system is adapted to identify a spam documentbased on the appearance of excessive number of related phrases in thedocument.

The present invention has further embodiments in system and softwarearchitectures, computer program products and computer implementedmethods, and computer generated user interfaces and presentations.

The foregoing are just some of the features of an information retrievalsystem and methodology based on phrases. Those of skill in the art ofinformation retrieval will appreciate the flexibility of generality ofthe phrase information allows for a large variety of uses andapplications in indexing, document annotation, searching, ranking, andother areas of document analysis and processing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram of the software architecture of one embodimentof the present invention.

FIG. 2 illustrates a method of identifying phrases in documents.

FIG. 3 illustrates a document with a phrase window and a secondarywindow.

FIG. 4 illustrates a method of identifying related phrases.

FIG. 5 illustrates a method of indexing documents for related phrases.

FIG. 6 illustrates a method of retrieving documents based on phrases.

The figures depict a preferred embodiment of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION OF THE INVENTION

I. System Overview

Referring now to FIG. 1, there is shown the software architecture of anembodiment of a search system 100 in accordance with one embodiment ofpresent invention. In this embodiment, the system includes an indexingsystem 110, a search system 120, a presentation system 130, and a frontend server 140.

The indexing system 110 is responsible for identifying phrases indocuments, and indexing documents according to their phrases, byaccessing various websites 190 and other document collections. The frontend server 140 receives queries from a user of a client 170, andprovides those queries to the search system 120. The search system 120is responsible for searching for documents relevant to the search query(search results), including identifying any phrases in the search query,and then ranking the documents in the search results using the presenceof phrases to influence the ranking order. The search system 120provides the search results to the presentation system 130. Thepresentation system 130 is responsible for modifying the search resultsincluding removing near duplicate documents, and generating topicaldescriptions of documents, and providing the modified search resultsback to the front end server 140, which provides the results to theclient 170. The system 100 further includes a primary index 150 and asecondary index 152 that stores the indexing information pertaining todocuments, and a phrase data store 160 that stores phrases, and relatedstatistical information. The primary index 150 is distributed across anumber of primary servers 1 . . . M1, and the secondary index 152 islikewise distributed across a number of secondary servers 1 . . . M2.

In the context of this application, “documents” are understood to be anytype of media that can be indexed and retrieved by a search engine,including web documents, images, multimedia files, text documents, PDFsor other image formatted files, and so forth. A document may have one ormore pages, partitions, segments or other components, as appropriate toits content and type. Equivalently a document may be referred to as a“page,” as commonly used to refer to documents on the Internet. Nolimitation as to the scope of the invention is implied by the use of thegeneric term “documents.” The search system 100 operates over a largecorpus of documents, such as the Internet and World Wide Web, but canlikewise be used in more limited collections, such as for the documentcollections of a library or private enterprises. In either context, itwill be appreciated that the documents are typically distributed acrossmany different computer systems and sites. Without loss of generalitythen, the documents generally, regardless of format or location (e.g.,which website or database) will be collectively referred to as a corpusor document collection. Each document has an associated identifier thatuniquely identifies the document; the identifier is preferably a URL,but other types of identifiers (e.g., document numbers) may be used aswell. In this disclosure, the use of URLs to identify documents isassumed.

II. Indexing System

In one embodiment, the indexing system 110 provides three primaryfunctional operations: 1) identification of phrases and related phrases,2) indexing of documents with respect to phrases, and 3) generation andmaintenance of a phrase-based taxonomy. Those of skill in the art willappreciate that the indexing system 110 will perform other functions aswell in support of conventional indexing functions, and thus these otheroperations are not further described herein. The indexing system 110operates on the primary index 150 and secondary index 152 and datarepository 160 of phrase data. These data repositories are furtherdescribed below.

1. Phrase Identification

The phrase identification operation of the indexing system 110identifies “good” and “bad” phrases in the document collection that areuseful to indexing and searching documents. In one aspect, good phrasesare phrases that tend to occur in more than certain percentage ofdocuments in the document collection, and/or are indicated as having adistinguished appearance in such documents, such as delimited by markuptags or other morphological, format, or grammatical markers. Anotheraspect of good phrases is that they are predictive of other goodphrases, and are not merely sequences of words that appear in thelexicon. For example, the phrase “President of the United States” is aphrase that predicts other phrases such as “George Bush” and “BillClinton.” However, other phrases are not predictive, such as “fell downthe stairs” or “top of the morning,” “out of the blue,” since idioms andcolloquisms like these tend to appear with many other different andunrelated phrases. Thus, the phrase identification phase determineswhich phrases are good phrases and which are bad (i.e., lacking inpredictive power).

Referring to now FIG. 2, the phrase identification process has thefollowing functional stages:

200: Collect possible and good phrases, along with frequency andco-occurrence statistics of the phrases.

202: Classify possible phrases to either good or bad phrases based onfrequency statistics.

204: Prune good phrase list based on a predictive measure derived fromthe co-occurrence statistics.

Each of these stages will now be described in further detail.

The first stage 200 is a process by which the indexing system 110 crawlsa set of documents in the document collection, making repeatedpartitions of the document collection over time. Cne partition isprocessed per pass. The number of documents crawled per pass can vary,and is preferably about 1,000,000 per partition. It is preferred thatonly previously uncrawled documents are processed in each partition,until all documents have been processed, or some other terminationcriteria is met. In practice, the crawling continues as new documentsare being continually added to the document collection. The followingsteps are taken by the indexing system 110 for each document that iscrawled.

Traverse the words of the document with a phrase window length of n,where n is a desired maximum phrase length. The length of the windowwill typically be at least 2, and preferably 4 or 5 terms (words).Preferably phrases include all words in the phrase window, includingwhat would otherwise be characterized as stop words, such as “a”, “the,”and so forth. A phrase window may be terminated by an end of line, aparagraph return, a markup tag, or other indicia of a change in contentor format.

FIG. 3 illustrates a portion of a document 300 during a traversal,showing the phrase window 302 starting at the word “stock” and extending5 words to the right. The first word in the window 302 is candidatephrase i, and the each of the sequences i+1, i+2, i+3, i+4, and i+5 islikewise a candidate phrase. Thus, in this example, the candidatephrases are: “stock”, “stock dogs”, “stock dogs for”, “stock dogs forthe”, “stock dogs for the Basque”, and “stock dogs for the Basqueshepherds”.

In each phrase window 302, each candidate phrase is checked in turn todetermine if it is already present in the good phrase list 208 or thepossible phrase list 206. If the candidate phrase is not present ineither the good phrase list 208 or the possible phrase list 206, thenthe candidate has already been determined to be “bad” and is skipped.

If the candidate phrase is in the good phrase list 208, as entry g_(j),then the index 150 entry for phrase g_(j) is updated to include thedocument (e.g., its URL or other document identifier), to indicate thatthis candidate phrase g_(j) appears in the current document. An entry inthe index 150 for a phrase g_(j) (or a term) is referred to as theposting list of the phrase g_(j). The posting list includes a list ofdocuments d (by their document identifiers, e.g. a document number, oralternatively a URL) in which the phrase occurs. In one embodiment, thedocument number is derived by a one-way hash of the URL, using, forexample, MD5.

In addition, the co-occurrence matrix 212 is updated, as furtherexplained below. In the very first pass, the good and bad lists will beempty, and thus, most phrases will tend to be added to the possiblephrase list 206.

If the candidate phrase is not in the good phrase list 208 then it isadded to the possible phrase list 206, unless it is already presenttherein. Each entry p on the possible phrase list 206 has threeassociated counts:

P(p): Number of documents on which the possible phrase appears;

S(p): Number of all instances of the possible phrase; and

M(p): Number of interesting instances of the possible phrase. Aninstance of a possible phrase is “interesting” where the possible phraseis distinguished from neighboring content in the document by grammaticalor format markers, for example by being in boldface, or underline, or asanchor text in a hyperlink, or in quotation marks. These (and other)distinguishing appearances are indicated by various HTML markup languagetags and grammatical markers. These statistics are maintained for aphrase when it is placed on the good phrase list 208.

In addition the various lists, a co-occurrence matrix 212 (G) for thegood phrases is maintained. The matrix G has a dimension of m×m, where mis the number of good phrases. Each entry G(j, k) in the matrixrepresents a pair of good phrases (g_(j), g_(k)). The co-occurrencematrix 212 logically (though not necessarily physically) maintains threeseparate counts for each pair (g_(j), g_(k)) of good phrases withrespect to a secondary window 304 that is centered at the current wordi, and extends +/−h words. In one embodiment, such as illustrated inFIG. 3, the secondary window 304 is 30 words. The co-occurrence matrix212 thus maintains:

R(j,k): Raw Co-occurrence count. The number of times that phrase g_(j)appears in a secondary window 304 with phrase g_(k);

D(j,k): Disjunctive Interesting count. The number of times that eitherphrase g_(j) or phrase g_(k) appears as distinguished text in asecondary window; and

C(j,k): Conjunctive Interesting count: the number of times that bothg_(j) and phrase g_(k) appear as distinguished text in a secondarywindow. The use of the conjunctive interesting count is particularlybeneficial to avoid the circumstance where a phrase (e.g., a copyrightnotice) appears frequently in sidebars, footers, or headers, and thus isnot actually predictive of other text.

Referring to the example of FIG. 3, assume that the “stock dogs” is onthe good phrase list 208, as well as the phrases “Australian Shepherd”and “Australian Shepard Club of America”. Both of these latter phrasesappear within the secondary window 304 around the current phrase “stockdogs”. However, the phrase “Australian Shepherd Club of America” appearsas anchor text for a hyperlink (indicated by the underline) to website.Thus the raw co-occurrence count for the pair {“stock dogs”, “AustralianShepherd”} is incremented, and the raw occurrence count and thedisjunctive interesting count for {“stock dogs”, “Australian ShepherdClub of America”} are both incremented because the latter appears asdistinguished text.

The process of traversing each document with both the sequence window302 and the secondary window 304, is repeated for each document in thepartition.

Once the documents in the partition have been traversed, the next stageof the indexing operation is to update 202 the good phrase list 208 fromthe possible phrase list 206. A possible phrase p on the possible phraselist 206 is moved to the good phrase list 208 if the frequency ofappearance of the phrase and the number of documents that the phraseappears in indicates that it has sufficient usage as semanticallymeaningful phrase.

In one embodiment, this is tested as follows. A possible phrase p isremoved from the possible phrase list 206 and placed on the good phraselist 208 if:

a) P(p)>10 and S(p)>20 (the number of documents containing phrase p ismore than 10, and the number of occurrences of phrase p is more then20); or

b) M(p)>5 (the number of interesting instances of phrase p is more than5).

These thresholds are scaled by the number of documents in the partition;for example if 2,000,000 documents are crawled in a partition, then thethresholds are approximately doubled. Of course, those of skill in theart will appreciate that the specific values of the thresholds, or thelogic of testing them, can be varied as desired.

If a phrase p does not qualify for the good phrase list 208, then it ischecked for qualification for being a bad phrase. A phrase p is a badphrase if:

a) number of documents containing phrase, P(p)<2; and

b) number of interesting instances of phrase, M(p)=0.

These conditions indicate that the phrase is both infrequent, and notused as indicative of significant content and again these thresholds maybe scaled per number of documents in the partition.

It should be noted that the good phrase list 208 will naturally includeindividual words as phrases, in addition to multi-word phrases, asdescribed above. This is because each the first word in the phrasewindow 302 is always a candidate phrase, and the appropriate instancecounts will be accumulated. Thus, the indexing system 110 canautomatically index both individual words (i.e., phrases with a singleword) and multiple word phrases. The good phrase list 208 will also beconsiderably shorter than the theoretical maximum based on all possiblecombinations of m phrases. In typical embodiment, the good phrase list208 will include about 6.5×10⁵ phrases. A list of bad phrases is notnecessary to store, as the system need only keep track of possible andgood phrases.

By the final pass through the document collection, the list of possiblephrases will be relatively short, due to the expected distribution ofthe use of phrases in a large corpus. Thus, if say by the 10^(th) pass(e.g., 10,000,000 documents), a phrase appears for the very first time,it is very unlikely to be a good phrase at that time. It may be newphrase just coming into usage, and thus during subsequent crawls becomesincreasingly common. In that case, its respective counts will increasesand may ultimately satisfy the thresholds for being a good phrase.

The third stage of the indexing operation is to prune 204 the goodphrase list 208 using a predictive measure derived from theco-occurrence matrix 212. Without pruning, the good phrase list 208 islikely to include many phrases that while legitimately appearing in thelexicon, themselves do not sufficiently predict the presence of otherphrases, or themselves are subsequences of longer phrases. Removingthese weak good phrases results in a very robust likely of good phrases.To identify good phrases, a predictive measure is used which expressesthe increased likelihood of one phrase appearing in a document given thepresence of another phrase. This is done, in one embodiment, as follows:

As noted above, the co-occurrence matrix 212 is an m×m matrix of storingdata associated with the good phrases. Each row j in the matrixrepresents a good phrase g_(j) and each column k represented a goodphrase g_(k). For each good phrase g_(j), an expected value E(g_(j)) iscomputed. The expected value E is the percentage of documents in thecollection expected to contain g_(j). This is computed, for example, asthe ratio of the number of documents containing g_(j) to the totalnumber T of documents in the collection that have been crawled: P(j)/T.

As noted above, the number of documents containing g_(j) is updated eachtime g_(j) appears in a document. The value for E(g_(j)) can be updatedeach time the counts for g_(j) are incremented, or during this thirdstage.

Next, for each other good phrase g_(k) (e.g., the columns of thematrix), it is determined whether g_(j) predicts g_(k). A predictivemeasure for g_(j) is determined as follows:

i) compute the expected value E(g_(k)). The expected co-occurrence rateE(j,k) of g_(j) and g_(k), if they were unrelated phrases is thenE(g_(j))*E(g_(k));

ii) compute the actual co-occurrence rate A(j,k) of g_(j) and g_(k).This is the raw co-occurrence count R(j, k) divided by T, the totalnumber of documents;

iii) g_(j) is said to predict g_(k) where the actual co-occurrence rateA(j,k) exceeds the expected co-occurrence rate E(j,k) by a thresholdamount.

In one embodiment, the predictive measure is information gain. Thus, aphrase g_(j) predicts another phrase g_(k) when the information gain Iof g_(k) in the presence of g_(j) exceeds a threshold. In oneembodiment, this is computed as follows:I(j,k)=A(j,k)/E(j,k)

And good phrase g_(j) predicts good phrase g_(k) where:

I(j,k)>Information Gain threshold.

In one embodiment, the information gain threshold is 1.5, but ispreferably between 1.1 and 1.7. Raising the threshold over 1.0 serves toreduce the possibility that two otherwise unrelated phrases co-occurmore than randomly predicted.

As noted the computation of information gain is repeated for each columnk of the matrix G with respect to a given row j. Once a row is complete,if the information gain for none of the good phrases g_(k) exceeds theinformation gain threshold, then this means that phrase g_(j) does notpredict any other good phrase. In that case, g_(j) is removed from thegood phrase list 208, essentially becoming a bad phrase. Note that thecolumn j for the phrase g_(j) is not removed, as this phrase itself maybe predicted by other good phrases.

This step is concluded when all rows of the co-occurrence matrix 212have been evaluated.

The final step of this stage is to prune the good phrase list 208 toremove incomplete phrases. An incomplete phrase is a phrase that onlypredicts its phrase extensions, and which starts at the left most sideof the phrase (i.e., the beginning of the phrase). The “phraseextension” of phrase p is a super-sequence that begins with phrase p.For example, the phrase “President of” predicts “President of the UnitedStates”, “President of Mexico”, “President of AT&T”, etc. All of theselatter phrases are phrase extensions of the phrase “President of” sincethey begin with “President of” and are super-sequences thereof.

Accordingly, each phrase g_(j) remaining on the good phrase list 208will predict some number of other phrases, based on the information gainthreshold previously discussed. Now, for each phrase g_(j) the indexingsystem 110 performs a string match with each of the phrases g_(k) thatis predicts. The string match tests whether each predicted phrase g_(k)is a phrase extension of the phrase g_(j). If all of the predictedphrases g_(k) are phrase extensions of phrase g_(j), then phrase g_(j)is incomplete, and is removed from the good phrase list 208, and addedto an incomplete phrase list 216. Thus, if there is at least one phraseg_(k) that is not an extension of g_(k), then g_(j) is complete, andmaintained in the good phrase list 208. For example then, “President ofthe United” is an incomplete phrase because the only other phrase thatit predicts is “President of the United States” which is an extension ofthe phrase.

The incomplete phrase list 216 itself is very useful during actualsearching. When a search query is received, it can be compared againstthe incomplete phase list 216. If the query (or a portion thereof)matches an entry in the list, then the search system 120 can lookup themost likely phrase extensions of the incomplete phrase (the phraseextension having the highest information gain given the incompletephrase), and suggest this phrase extension to the user, or automaticallysearch on the phrase extension. For example, if the search query is“President of the United,” the search system 120 can automaticallysuggest to the user “President of the United States” as the searchquery.

After the last stage of the indexing process is completed, the goodphrase list 208 will contain a large number of good phrases that havebeen discovered in the corpus. Each of these good phrases will predictat least one other phrase that is not a phrase extension of it. That is,each good phrase is used with sufficient frequency and independence torepresent meaningful concepts or ideas expressed in the corpus. Unlikeexisting systems which use predetermined or hand selected phrases, thegood phrase list reflects phrases that actual are being used in thecorpus. Further, since the above process of crawling and indexing isrepeated periodically as new documents are added to the documentcollection, the indexing system 110 automatically detects new phrases asthey enter the lexicon.

2. Identification of Related Phrases and Clusters of Related Phrases

Referring to FIG. 4, the related phrase identification process includesthe following functional operations.

400: Identify related phrases having a high information gain value.

402: Identify clusters of related phrases.

404: Store cluster bit vector and cluster number.

Each of these operations is now described in detail.

First, recall that the co-occurrence matrix 212 contains good phrasesg_(j), each of which predicts at least one other good phrase g_(k) withan information gain greater than the information gain threshold. Toidentify 400 related phrases then, for each pair of good phrases (g_(j),g_(k)) the information gain is compared with a Related Phrase threshold,e.g., 100. That is, g_(j) and g_(k) are related phrases where:I(g _(j) ,g _(k))>100.

This high threshold is used to identify the co-occurrences of goodphrases that are well beyond the statistically expected rates.Statistically, it means that phrases g_(j) and g_(k) co-occur 100 timesmore than the expected co-occurrence rate. For example, given the phrase“Monica Lewinsky” in a document, the phrase “Bill Clinton” is a 100times more likely to appear in the same document, then the phrase “BillClinton” is likely to appear on any randomly selected document. Anotherway of saying this is that the accuracy of the predication is 99.999%because the occurrence rate is 100:1.

Accordingly, any entry (g_(j), g_(k)) that is less the Related Phrasethreshold is zeroed out, indicating that the phrases g_(i), g_(k) arenot related. Any remaining entries in the co-occurrence matrix 212 nowindicate all related phrases.

The columns g_(k) in each row g_(j) of the co-occurrence matrix 212 arethen sorted by the information gain values I(g_(j), g_(k)), so that therelated phrase g_(k) with the highest information gain is listed first.This sorting thus identifies for a given phrase g_(j), which otherphrases are most likely related in terms of information gain.

The next step is to determine 402 which related phrases together form acluster of related phrases. A cluster is a set of related phrases inwhich each phrase has high information gain with respect to at least oneother phrase. In one embodiment, clusters are identified as follows.

In each row g_(j) of the matrix, there will be one or more other phrasesthat are related to phrase g_(j). This set is related phrase set R_(j),where R={g_(k), g_(l), . . . g_(m)}.

For each related phrase m in R_(j), the indexing system 110 determinesif each of the other related phrases in R is also related to g_(j).Thus, if I(g_(k), g_(l)) is also non-zero, then g_(j), g_(k), and g_(l)are part of a cluster. This cluster test is repeated for each pair(g_(l), g_(m)) in R.

For example, assume the good phrase “Bill Clinton” is related to thephrases “President”, “Monica Lewinsky”, because the information gain ofeach of these phrases with respect to “Bill Clinton” exceeds the RelatedPhrase threshold. Further assume that the phrase “Monica Lewinsky” isrelated to the phrase “purse designer”. These phrases then form the setR. To determine the clusters, the indexing system 110 evaluates theinformation gain of each of these phrases to the others by determiningtheir corresponding information gains. Thus, the indexing system 110determines the information gain I(“President”, “Monica Lewinsky”),I(“President”, “purse designer”), and so forth, for all pairs in R. Inthis example, “Bill Clinton,” “President”, and “Monica Lewinsky” form aone cluster, “Bill Clinton,” and “President” form a second cluster, and“Monica Lewinsky” and “purse designer” form a third cluster, and “MonicaLewinsky”, “Bill Clinton,” and “purse designer” form a fourth cluster.This is because while “Bill Clinton” does not predict “purse designer”with sufficient information gain, “Monica Lewinsky” does predict both ofthese phrases.

To record 404 the cluster information, each cluster is assigned a uniquecluster number (cluster ID). This information is then recorded inconjunction with each good phrase g_(j).

In one embodiment, the cluster number is determined by a cluster bitvector that also indicates the orthogonality relationships between thephrases. The cluster bit vector is a sequence of bits of length n, thenumber of good phrases in the good phrase list 208. For a given goodphrase g_(j), the bit positions correspond to the sorted related phrasesR of g_(j). A bit is set if the related phrase g_(k) in R is in the samecluster as phrase g_(j). More generally, this means that thecorresponding bit in the cluster bit vector is set if there isinformation gain in either direction between g_(j) and g_(k).

The cluster number then is the value of the bit string that results.This implementation has the property that related phrases that havemultiple or one-way information gain appear in the same cluster.

An example of the cluster bit vectors are as follows, using the abovephrases: Monica purse Cluster Bill Clinton President Lewinsky designerID Bill Clinton 1 1 1 0 14 President 1 1 0 0 12 Monica 1 0 1 1 11Lewinsky purse 0 0 1 1 3 designer

To summarize then, after this process there will be identified for eachgood phrase g_(j), a set of related phrases R, which are sorted in orderof information gain I(g_(j), g_(k)) from highest to lowest. In addition,for each good phrase g_(j), there will be a cluster bit vector, thevalue of which is a cluster number identifying the primary cluster ofwhich the phrase g_(j) is a member, and the orthogonality values (1 or 0for each bit position) indicating which of the related phrases in R arein common clusters with g_(j). Thus in the above example, “BillClinton”, “President”, and “Monica Lewinsky” are in cluster 14 based onthe values of the bits in the row for phrase “Bill Clinton”.

To store this information, two basic representations are available.First, as indicated above, the information may be stored in theco-occurrence matrix 212, wherein:

entry G[row j, col. k]=(I(j,k), clusterNumber, clusterBitVector)

Alternatively, the matrix representation can be avoided, and allinformation stored in the good phrase list 208, wherein each row thereinrepresents a good phrase g_(j):

Phrase row_(j)=list [phrase g_(k), (I(j,k), clusterNumber,clusterBitVector)].

This approach provides a useful organization for clusters. First, ratherthan a strictly—and often arbitrarily—defined hierarchy of topics andconcepts, this approach recognizes that topics, as indicated by relatedphrases, form a complex graph of relationships, where some phrases arerelated to many other phrases, and some phrases have a more limitedscope, and where the relationships can be mutual (each phrase predictsthe other phrase) or one-directional (one phrase predicts the other, butnot vice versa). The result is that clusters can be characterized“local” to each good phrase, and some clusters will then overlap byhaving one or more common related phrases.

For a given good phrase g_(j) then the ordering of the related phrasesby information gain provides a taxonomy for naming the clusters of thephrase: the cluster name is the name of the related phrase in thecluster having the highest information gain.

The above process provides a very robust way of identifying significantphrases that appear in the document collection, and beneficially, theway these related phrases are used together in natural “clusters” inactual practice. As a result, this data-driven clustering of relatedphrases avoids the biases that are inherent in any manually directed“editorial” selection of related terms and concepts, as is common inmany systems.

3. Indexing Documents with Phrases and Related Phrases

Given the good phrase list 208, including the information pertaining torelated phrases and clusters, the next functional operation of theindexing system 110 is to index documents in the document collectionwith respect to the good phrases and clusters, and store the updatedinformation in the primary index 150 and the secondary index 152. FIG. 5illustrates this process, in which there are the following functionalstages for indexing a document:

500: Post document to the posting lists of good phrases found in thedocument.

502: Update instance counts and related phrase bit vector for relatedphases and secondary related phrases.

504: Reorder index entries according to posting list size.

506: Rank index entries in each posting list by an information retrievalscore or feature value.

508: Partition each posting list between the primary server 150 and asecondary server 152.

These stages are now described in further detail.

A set of documents is traversed or crawled, as before; this may be thesame or a different set of documents. For a given document d, traverse500 the document word by word with a sequence window 302 of length n,from position i, in the manner described above.

In a given phrase window 302, identify all good phrases in the window,starting at position i. Each good phrase is denoted as g_(i). Thus, g1is the first good phrase, g2 would be the second good phrase, and soforth.

For each good phrase g_(i) (example g1 “President” and g4 “President ofATT”) post the document identifier (e.g., the URL) to the posting listfor the good phrase g_(i) in the index 150. This update identifies thatthe good phrase g_(i) appears in this specific document.

In one embodiment, the posting list for a phrase g_(j) takes thefollowing logical form:

Phrase g_(j): list: (document d, [list: related phase counts] [relatedphrase information])

For each phrase g_(j) there is a list of the documents d on which thephrase appears. For each document, there is a list of counts of thenumber of occurrences of the related phrases R of phrase g_(j) that alsoappear in document d.

In one embodiment, the related phrase information is a related phase bitvector. This bit vector may be characterized as a “bi-bit” vector, inthat for each related phrase g_(k) there are two bit positions, g_(k)-1,g_(k)-2. The first bit position stores a flag indicating whether therelated phrase g_(k) is present in the document d (i.e., the count forg_(k) in document d is greater than 0). The second bit position stores aflag that indicates whether a related phrase g_(l) of g_(k) is alsopresent in document d. The related phrases g_(l) of a related phraseg_(k) of a phrase g_(j) are herein called the “secondary related phrasesof g_(j)”. The counts and bit positions correspond to the canonicalorder of the phrases in R (sorted in order of decreasing informationgain). This sort order has the effect of making the related phrase g_(k)that is most highly predicted by g_(j) associated with the mostsignificant bit of the related phrase bit vector, and the related phraseg_(l) that is least predicted by g_(j) associated with the leastsignificant bit.

It is useful to note that for a given phrase g, the length of therelated phrase bit vector, and the association of the related phrases tothe individual bits of the vector, will be the same with respect to alldocuments containing g. This implementation has the property of allowingthe system to readily compare the related phrase bit vectors for any (orall) documents containing g, to see which documents have a given relatedphrase. This is beneficial for facilitating the search process toidentify documents in response to a search query. Accordingly, a givendocument will appear in the posting lists of many different phrases, andin each such posting list, the related phrase vector for that documentwill be specific to the phrase that owns the posting list. This aspectpreserves the locality of the related phrase bit vectors with respect toindividual phrases and documents.

Accordingly, the next stage 502 includes traversing the secondary window304 of the current index position in the document (as before a secondarywindow of +/−K terms, for example, 30 terms), for example from i-K toi+K. For each related phrase g_(k) of g_(i) that appears in thesecondary window 304, the indexing system 110 increments the count ofg_(k) with respect to document d in the related phrase count. If g_(i)appears later in the document, and the related phrase is found againwithin the later secondary window, again the count is incremented.

As noted, the corresponding first bit g_(k)-1 in the related phrase bitmap is set based on the count, with the bit set to 1 if the count forg_(k) is >0, or set to 0 if the count equals 0.

Next, the second bit, g_(k)-2 is set by looking up related phrase g_(k)in the index 150, identifying in g_(k)'s posting list the entry fordocument d, and then checking the secondary related phrase counts (orbits) for g_(k) for any its related phrases. If any of these secondaryrelated phrases counts/bits are set, then this indicates that thesecondary related phrases of g_(j) are also present in document d.

When document d has been completely processed in this manner, theindexing system 110 will have identified the following:

i) each good phrase g_(j) in document d;

ii) for each good phrase g_(j) which of its related phrases g_(k) arepresent in document d;

iii) for each related phrase g_(k) present in document d, which of itsrelated phrases g_(l) (the secondary related phrases of g_(j)) are alsopresent in document d.

a) Partitioned Indexing

Each phrase in the index 150 is given a phrase number, based on itsfrequency of occurrence in the corpus. The more common the phrase, thelower phrase number it receives in the index. The indexing system 110then sorts 504 all of the posting lists 214 in the primary index 150 indeclining order according to the number of documents listed in eachposting list, so that the most frequently occurring phrases have thelowest phrase number and are listed first in the primary index 150. Asnoted above, the primary index 150 is distributed across M1 primaryservers. To reduce disk contention, phrases are distributed across thesemachines by hash function, e.g., phase_number MOD M1.

To significantly increase the number of documents that can be indexed bythe system, the primary index 150 is further processed to selectivelypartition each of the posting lists 214. As noted above, the postinglist of each phrase contains a list of documents. Each document in theposting list is given 506 an information retrieval-type score withrespect to the phrase. However the score is computed, the documents inthe posting list are then ranked in declining order by this score, withthe highest scoring documents listed first in the posting list. Thispre-ranking of documents is particularly beneficial for improvedperformance when retrieving documents in response to a search query.

The scoring algorithm for pre-ranking the documents may be the sameunderlying relevance scoring algorithm used in the search system 120 togenerate a relevance score. In one embodiment, the IR score is based onthe page rank algorithm, as described in U.S. Pat. No. 6,285,999.Alternatively or additionally, statistics for a number of IR-relevantattributes of the document, such as the number of inlinks, outlinks,document length, may also be stored, and used alone or in combination inorder to rank the documents. For example, the documents may be ranked indeclining order according to the number of inlinks. To furtherfacilitate the fastest possible retrieval of information from theprimary index 150, the entries in each posting list 214 are physicallystored on the appropriate primary server in the rank ordering by theIR-type score.

Given that the highest scoring documents for a given phrase are now atthe beginning of the posting list, the posting list 214 is partitioned508 between the primary index 150 and the secondary index 152. Theposting list entries for up to the first K documents remain stored onthe primary server 150, while the posting list entries for the remainingn>K documents are stored in the secondary index 152, and deleted fromthe end of the posting list 214 in the primary index 150. In oneembodiment K is set to 32,768 (32 k), but a higher or lower value of Kmay be used. A phrase that has its posting list partitioned between theprimary and the secondary index is called a ‘common’ phrase, whereas aphrase that is not partitioned is called a ‘rare’ phrase. The portion ofa posting list stored in the primary index 150 is referred to as theprimary posting list, and contains the primary entries, and portion of aposting list stored in the secondary index 152 is referred to as thesecondary posting list and contains the secondary entries. The secondaryentries for a given posting list 214 are assigned to a secondary serveraccording to another hash function of the phrase number, e.g., phrasenumber MOD M2. The secondary server ID is stored in the posting list onthe primary server, to allow the search system 120 to readily access theappropriate secondary server as needed. For each phrase posting liststored on one of the secondary servers, the secondary entries are storedphysically in order of their document numbers, from lowest documentnumber to highest (in contrast to the relevance ordering in the primaryindex 150). Preferably, no relevance information is stored in thesecondary entries, so that the entries contain a minimal amount of data,such as the document number, and document locator (e.g., URL). Theranking and partitioning steps may be performed sequentially for eachphrase; alternatively all (or a number of) phrases can first be ranked,and then partitioned; the algorithm design is merely a design choice andthe above variations are considered equivalents. The ranking andpartitioning steps are conducted during each indexing pass over a set ofdocuments, so that any phrases that are updated with new documentsduring an indexing pass are re-ranked and re-partitioned. Otheroptimizations and operations are also possible.

In one embodiment, the selection of document attributes that are storedin the primary index 150 for each document in the post listing 214 isvariable, and in particular decreases towards the end of the postinglist 214 in the primary index. In other words, documents that are highlyranked in the posting list based on their relevance score (or otherrelevance based attributes), will have all or most of the documentattributes stored in the document entry in the posting list. Documentsat near the end of the posting list 214 in the primary index will haveonly a more limited set of such attributes stored.

In one embodiment, each posting list 214 in the primary index 150 hasthree sections (or tiers), of lengths m, 3m, 5m, where m here is anumber of document entries, In this embodiment, it is desirable thateach section have length K, as described above, that is m=K, and theentire primary index has 9K entries; the secondary index would thenstore the secondary entries where n>9K.

In the first section (first m entries), the following relevanceattributes are stored for each document entry in the posting list of agiven phrase:

-   -   1. The document relevance score (e.g., page rank);    -   2. Total number of occurrences of the phrase in the document;    -   3. A rank ordered list of up to 10,000 anchor documents that        also contain the phrase and which point to this document, and        for each anchor document its relevance score (e.g., page rank),        and the anchor text itself; and    -   4. The position of each phrase occurrence, and for each        occurrence, a set of flags indicating whether the occurrence is        a title, bold, a heading, in a URL, in the body, in a sidebar,        in a footer, in an advertisement, capitalized, or in some other        type of HTML markup.

In the second section (next 3m entries), only items 1-3 are stored.

In the third section (final 5m entries), only item 1 is stored.

Systematically reducing which document attributes are stored in laterportions of each posting list 214 is acceptable because documents atnear the end of the posting list are already determined to be lessrelevant to the particular phrase (lower relevance score), and so it isnot entirely necessary to store all of their relevance characteristics.

The foregoing storage arrangement enables storing significantly moreentries in a given amount of hard disk storage than conventionaltechniques. First, elimination of the term position information forevery phrase in every document provides approximately a 50% reduction inthe amount of storage needed for a given set of documents, therebyeffectively doubling the number of documents that can be stored. Second,partitioning the posting lists between the primary index and secondaryindices and storing relevance information only in the primary indexprovides further substantial savings. Many phrases have over 100,000,even 1,000,000 documents in their posting lists. Storing the relevanceinformation for only a limited number of entries in the primary indexeliminates the storage needed for the documents that are not likely tobe returned in search. This aspect provides approximately a ten-foldincrease in the number of documents that can be stored. Finally, furthersavings (approximately 25%-50% reduction in required storage capacity)are achieved by selectively storing less relevance information in theprimary index 150 for the less relevant (lower ranked) documents in eachposting list 214.

b) Determining the Topics for a Document

The indexing of documents by phrases and use of the clusteringinformation provides yet another advantage of the indexing system 110,which is the ability to determine the topics that a document is aboutbased on the related phrase information.

Assume that for a given good phrase g_(j) and a given document d, theposting list entry is as follows:

g_(j): document d: related phrase counts:={3,4,3,0,0,2,1,1,0}

-   -   related phrase bit vector:={11 11 10 00 00 10 10 10 01}

where, the related phrase bit vector is shown in the bi-bit pairs.

From the related phrase bit vector, we can determine primary andsecondary topics for the document d. A primary topic is indicated by abit pair (1,1), and a secondary topic is indicated by a bit pair (1,0).A related phrase bit pair of (1,1) indicates that both the relatedphrase g_(k) for the bit pair is present in document d, along thesecondary related phrases g_(l) as well. This may be interpreted to meanthat the author of the document d used several related phrases g_(j),g_(k), and g_(l) together in drafting the document. A bit pair of (1,0)indicates that both g_(j) and g_(k) are present, but no furthersecondary related phrases from g_(k) are present, and thus this is aless significant topic.

c) Indexing Instances of Documents for Archival Retrieval

Another embodiment of the present invention allows the capability tostore and maintain historical documents in the indices, and therebyenable archival retrieval of date specific instances (versions) ofindividual documents or pages. This capability has various beneficialuses, including enabling a user may search for documents within aspecific range of dates, enabling the search system 120 to use date orversion related relevance information in evaluating documents inresponse to a search query, and in organizing search results.

In this embodiment, the document identifier encodes the identity of thedocument with respect to a date interval. The first time a document iscrawled by the indexing system 110, the document identifier is stored asa hash of the document URL and the date stamp of the document, forexample, MD5 (URL, first date). Associated with the particular instanceof the document is date range field, which comprises a range of datesfor which the document instance is deemed to valid. The date range canbe specified as a date pair comprising a first date on which thedocument is deemed valid (the indexing date) and a last date on whichthe document is deemed valid (e.g., 11-01-04; 12-15-04). Alternatively,the date range can be specified as a first date, and a number indicatinga number of days following the first date (e.g., 11-01-04, 45). A datecan be specified in any useful format, including date strings or daynumbers. During the period in which the document is the currently validdocument, the second value is a status flag or token (including a NULLvalue), indicating this state; this is called the current interval. Forexample, (11-01-04, “open”) indicates that the document is currentlyvalid. This indicates that the document will satisfy search thatincludes a date limitation after the first date. Regardless of theparticular implementation, the first date for a given date interval maybe referred to as the “open date”, and the last date for a giveninterval may be referred to as the “closed date”.

During subsequent indexing passes by the indexing system 110, theindexing system 110 determines whether the document has changed. Ifthere is no change in the document, then the indexing system 110 takesno further action with respect to document. If there has been a changein the document (thus a new instance or version of the document), thenthe indexing system 110 re-indexes the document. Upon re-indexing, theindexing system 110 closes the current interval, by changing the openstatus flag to the current date minus one day. For example, if theindexing system 110 indexes the document on Dec. 16, 2004 and determinesthat the document has changed, then current interval is closed asfollows: (11-01-04, 12-15-04), and a new current interval is created,e.g., (12-16-04, “open”). The indexing system 110 maintains each of thedate ranges for the document, along with corresponding indexed relevancedata (e.g., phrases, relevance statistics, document inlinks, and soforth) for the date range. Thus, each date range and set of relevancedata is associated with a particular instance or version of thedocument. For each of date interval for a given document, the indexingsystem maintains a unique document identifier, e.g., MD5 (URL, firstdate), so as to be able to retrieve the appropriate cached documentinstance. In an embodiment using the primary and secondary indexes, whenan indexing pass is completed, the posting lists 214 in the primaryindex are rescored, re-ranked, and repartitioned.

The determination of whether a given document has changed since the lastindexing pass may be made in any number of ways, including usingstatistical rules, grammatical rules, or similar heuristics. In oneembodiment, the indexing system 110 uses the phrases of a document todetermine if a document has changed. Each time a document is indexed,the top N topics are identified and maintained as a list in associationwith the date range information, for example, the top 20 topics for thedate range (11-04-04, 12-15-04). The topic list of instance beingindexed is then compared with the topic list of a prior documentinstance, preferably the most recently closed date range. If more than M% of the topics have changed (e.g., 5%), then the document is deemed tohave changed, and is re-indexed for all phrases. It should be noted thatother methods of determining whether a document has changed may also beused, and that the use of phrase-based indexing is not required. Forexample, a set of statistical rules may be used based on changes indocument length, changes in which terms are most frequent, changes interm frequency, changes in the amount of types of HTML markup, or othermeasures of document structure or content.

III. Search System

The search system 120 operates to receive a query and search fordocuments relevant to the query, and provide a list of these documents(with links to the documents) in a set of search results. FIG. 6illustrates the main functional operations of the search system 120:

600: Identify phrases in the query.

602: Retrieve documents relevant to query phrases.

604: Rank documents in search results according to phrases.

The details of each of these of these stages is as follows.

1. Identification of Phrases in the Query and Query Expansion

The first stage 600 of the search system 120 is to identify any phrasesthat are present in the query in order to effectively search the index.The following terminology is used in this section:

q: a query as input and receive by the search system 120.

Qp: phrases present in the query.

Qr: related phrases of Qp.

Qe: phrase extensions of Qp.

Q: the union of Qp and Qr.

A query q is received from a client 190, having up to some maximumnumber of characters or words.

A phrase window of size N (e.g., 5) is used by the search system 120 totraverse the terms of the query q. The phrase window starts with thefirst term of the query, extends N terms to the right. This window isthen shifted right M-N times, where M is the number of terms in thequery.

At each window position, there will be N terms (or fewer) terms in thewindow. These terms constitute a possible query phrase. The possiblephrase is looked up in the good phrase list 208 to determine if it is agood phrase or not. If the possible phrase is present in the good phraselist 208, then a phrase number is returned for phrase; the possiblephrase is now a candidate phrase.

After all possible phrases in each window have been tested to determineif they are good candidate phrases, the search system 120 will have aset of phrase numbers for the corresponding phrases in the query. Thesephrase numbers are then sorted (declining order).

Starting with the highest phrase number as the first candidate phrase,the search system 120 determines if there is another candidate phrasewithin a fixed numerical distance within the sorted list, i.e., thedifference between the phrase numbers is within a threshold amount, e.g.20,000. If so, then the phrase that is leftmost in the query is selectedas a valid query phrase Qp. This query phrase and all of its sub-phrasesis removed from the list of candidates, and the list is resorted and theprocess repeated. The result of this process is a set of valid queryphrases Qp.

For example, assume the search query is “Hillary Rodham Clinton Bill onthe Senate Floor”. The search system 120 would identify the followingcandidate phrases, “Hillary Rodham Clinton Bill on,” “Hillary RodhamClinton Bill,” and “Hillary Rodham Clinton”. The first two arediscarded, and the last one is kept as a valid query phrase. Next thesearch system 120 would identify “Bill on the Senate Floor”, and thesubsphrases “Bill on the Senate”, “Bill on the”, “Bill on”, “Bill”, andwould select “Bill” as a valid query phrase Qp. Finally, the searchsystem 120 would parse “on the senate floor” and identify “Senate Floor”as a valid query phrase.

Next, the search system 120 adjusts the valid phrases Qp forcapitalization. When parsing the query, the search system 120 identifiespotential capitalizations in each valid phrase. This may be done using atable of known capitalizations, such as “united states” beingcapitalized as “United States”, or by using a grammar basedcapitalization algorithm. This produces a set of properly capitalizedquery phrases.

The search system 120 then makes a second pass through the capitalizedphrases, and selects only those phrases are leftmost and capitalizedwhere both a phrase and its subphrase is present in the set. Forexample, a search on “president of the united states” will becapitalized as “President of the United States”.

In the next stage, the search system 120 identifies 602 the documentsthat are relevant to the query phrases Q. The search system 120 thenretrieves the posting lists of the query phrases Q, and where necessary,intersects these lists to determine which documents appear on the all(or some number) of the posting lists for the query phrases. If a phraseQ in the query has a set of phrase extensions Qe (as further explainedbelow), then the search system 120 first forms the union of the postinglists of the phrase extensions, prior to doing the intersection with theposting lists. The search system 120 identifies phrase extensions bylooking up each query phrase Q in the incomplete phrase list 216, asdescribed above.

Using the primary index 150 and the secondary 150, the search system 120can further optimize the intersection operation. There are four generalcases of intersection analysis that the search system 120 has to handlebased on whether the query phrases are common or rare.

The first case is for single query phrase, which can be either common orrare. In this case, the search system 120 passes a selected number(e.g., 100 or 1000) of the first entries in the phrase's posting listfrom the primary index 150 to the ranking phase 604 for final ranking.The ranking phase can optimize the ranking operation since the documentsare already in rank order. Alternatively, since these are alreadypre-ranked by their relevance to the phrase, the set of documents can bedirectly provided as the search results, providing essentiallyinstantaneous results to the user.

The second case is where there are two common query phrases. Here, thesearch system 120 accesses the posting lists 214 for each phrase in theprimary index 150 and intersects these lists to form the final documentlist, which is then passed to the ranking phrase 604 for relevancescoring based on the set of relevance attributes associated withdocument. Because there are at least K documents in each posting list,there is a very high likelihood of a sufficient number documentscontaining both phrases, and thus intersection of the secondary entriesin the secondary index 152 is not necessary. This further reduces theamount of time needed for retrieval.

The third case is where there are two rare query phrases. This case istreated in the same manner as the second care, since here the entireposting list for each phrase is stored in the primary index.

The final case is where the valid query phrases comprise a common phraseand a rare phrase. In this case, the search system 120 first intersectsthe posting lists 214 from the primary index 150 for both phrases toform a first set or common documents. Next, the search system 120intersects the posting list for the rare phrase with the secondaryentries for the common phrase (which are already sorted in documentnumber order) to form a second set of common documents. The two sets areconjoined and then passed to ranking phase.

All instances where there are three or more query phrases can bereductively handled by one successive intersections using the abovemethods.

2. Ranking

a) Ranking Documents Based on Contained Phrases

The search system 120 provides a ranking stage 604 in which thedocuments in the search results are ranked, using the relevanceinformation and document attributes, along with the phrase informationin each document's related phrase bit vector, and the cluster bit vectorfor the query phrases. This approach ranks documents according to thephrases that are contained in the document, or informally “body hits.”

As described above, for any given phrase g_(j), each document d in theg_(j)'s posting list has an associated related phrase bit vector thatidentifies which related phrases g_(k) and which secondary relatedphrases g_(l) are present in document d. The more related phrases andsecondary related phrases present in a given document, the more bitsthat will be set in the document's related phrase bit vector for thegiven phrase. The more bits that are set, the greater the numericalvalue of the related phrase bit vector.

Accordingly, in one embodiment, the search system 120 sorts thedocuments in the search results according to the value of their relatedphrase bit vectors. The documents containing the most related phrases tothe query phrases Q will have the highest valued related phrase bitvectors, and these documents will be the highest-ranking documents inthe search results.

This approach is desirable because semantically, these documents aremost topically relevant to the query phrases. Note that this approachprovides highly relevant documents even if the documents do not containa high frequency of the input query terms q, since related phraseinformation was used to both identify relevant documents, and then rankthese documents. Documents with a low frequency of the input query termsmay still have a large number of related phrases to the query terms andphrases and thus be more relevant than documents that have a highfrequency of just the query terms and phrases but no related phrases.

In a second embodiment, the search system 120 scores each document inthe result set according which related phrases of the query phrase Q itcontains. This is done as follows:

Given each query phrase. Q, there will be some number N of relatedphrases Qr to the query phrase, as identified during the phraseidentification process. As described above, the related query phrases Qrare ordered according to their information gain from the query phrase Q.These related phrases are then assigned points, started with N pointsfor the first related phrase Qr1 (i.e., the related phrase Qr with thehighest information gain from Q), then N−1 points for the next relatedphrase Qr2, then N−2 points for Qr3, and so on, so that the last relatedphrase QrN is assigned 1 point.

Each document in the search results is then scored by determining whichrelated phrases Qr of the query phrase Q are present, and giving thedocument the points assigned to each such related phrase Qr. Thedocuments are then sorted from highest to lowest score.

As a further refinement, the search system 120 can cull certaindocuments from the result set. In some cases documents may be about manydifferent topics; this is particularly the case for longer documents. Inmany cases, users prefer documents that are strongly on point withrespect to a single topic expressed in the query over documents that arerelevant to many different topics.

To cull these latter types of documents, the search system 120 uses thecluster information in the cluster bit vectors of the query phrases, andremoves any document in which there are more than a threshold number ofclusters in the document. For example, the search system 120 can removeany documents that contain more than two clusters. This clusterthreshold can be predetermined, or set by the user as a searchparameter.

b) Ranking Documents Based on Anchor Phrases

In addition to ranking the documents in the search results based on bodyhits of query phrases Q, in one embodiment, the search system 120 alsoranks the documents based on the appearance of query phrases Q andrelated query phrases Qr in anchors to other documents. In oneembodiment, the search system 120 calculates a score for each documentthat is a function (e.g., linear combination) of two scores, a body hitscore and an anchor hit score.

For example, the document score for a given document can be calculatedas follows:Score=0.30*(body hit score)+0.70*(anchor hit score).

The weights of 0.30 and 0.70 can be adjusted as desired. The body hitscore for a document is the numerical value of the highest valuedrelated phrase bit vector for the document, given the query phrases Qp,in the manner described above. Alternatively, this value can directlyobtained by the search system 120 by looking up each query phrase Q inthe index 150, accessing the document from the posting list of the queryphrase Q, and then accessing the related phrase bit vector.

The anchor hit score of a document d a function of the related phrasebit vectors of the query phrases Q, where Q is an anchor term in adocument that references document d. When the indexing system 110indexes the documents in the document collection, it maintains for eachphrase a list of the documents in which the phrase is anchor text in anoutlink, and also for each document a list of the inlinks (and theassociated anchor text) from other documents. The inlinks for a documentare references (e.g. hyperlinks) from other documents (referencingdocuments) to a given document.

To determine the anchor hit score for a given document d then, thesearch system 120 iterates over the set of referencing documents R (i=1to number of referencing documents) listed in index by their anchorphrases Q, and sums the following product:R_(i).Q.Related phrase bit vector*D.Q.Related phrase bit vector.

The product value here is a score of how topical anchor phrase Q is todocument D. This score is here called the “inbound score component.”This product effectively weights the current document D's related bitvector by the related bit vectors of anchor phrases in the referencingdocument R. If the referencing documents R themselves are related to thequery phrase Q (and thus, have a higher valued related phrase bitvector), then this increases the significance of the current document Dscore. The body hit score and the anchor hit score are then combined tocreate the document score, as described above.

Next, for each of the referencing documents R, the related phrase bitvector for each anchor phrase Q is obtained. This is a measure of howtopical the anchor phrase Q is to the document R. This value is herecalled the outbound score component.

From the index 150 then, all of the (referencing document, referenceddocument) pairs are extracted for the anchor phrases Q. These pairs arethen sorted by their associated (outbound score component, inbound scorecomponent) values. Depending on the implementation, either of thesecomponents can be the primary sort key, and the other can be thesecondary sort key. The sorted results are then presented to the user.Sorting the documents on the outbound score component makes documentsthat have many related phrases to the query as anchor hits, rank mosthighly, thus representing these documents as “expert” documents. Sortingon the inbound document score makes documents that frequently referencedby the anchor terms the most high ranked.

c) Ranking Documents based on Date Range Relevance

The search system 120 can use the date range information in several waysduring the search and ranking operations. First, the search system 120can use the date range as an explicit search delimiter. For example mayinclude terms or phrases and a date, such as “United States Patent andTrademark Office 12/04/04”. The search system 120 can identify the dateterm, and then select documents that have the desired phrase and whichare indexed for a date range that includes the date term in the query.From the selected documents, the search system 120 can then obtainrelevance score for each document using the indexed relevance dataassociated with the date range. In this manner, an older or previousinstance of document may be retrieved instead of the current instancewhere it is more relevant to the search query. This is particularlyuseful for documents and pages that change frequently, such as the homepages of news sites and other sites containing frequently changinginformation.

Second, where no date term is included in a search query, the searchsystem 120 can use the date information in the index during relevanceranking, by weighting document relevance scores according to how oldthey are, so that older documents have their relevance scores downweighted (or newer documents are more highly weighted). Alternatively,in some cases, it is older versions of a document that are most relevantto a topic, rather than the most current version of a document. Forexample, news portal sites contemporaneously created at the time ofhistorical events are likely to be more relevant to a specific queryabout the event, then current instances of the new portal. In this case,the search system 120 can upweight older document instances, where forexample, the pattern of document relevance scores for all of theinstances of a document shows an increase around some historical date,followed by decreasing relevance scores for more current instances ofthe document.

Where one or more date terms are included in the search query, as above,documents may have their relevance scores down weighted in proportion tothe difference between the date term and the document date range, sothat documents that are either much older than the date range (measuredfrom either the open or the close date) or much newer than the desireddate terms have their relevance scored down weighted. Conversely, arelevance score can be increased instead of down weighted where the daterange for the document is closer to the desired date.

Third, the search system 120 can use the date range information aseither a primary or secondary factor for ordering the search results.For examples, documents can be grouped in reversed chronological order(e.g. monthly groups), and within each group, the documents can belisted from most to least relevant to the search query.

Another use of the data range information is to rank documents based onthe frequency with which they are updated. The search system 120 candetermine the number of instances of a given document (e.g., number ofdiscrete date ranges) over an interval of time (this count can bemaintained during indexing). The number of instances is then used toupweight those documents which are more frequently updated.

IV. Identifying Spam Documents

In another aspect the invention provides system and methods foridentifying spam documents as they are being indexed and when queriesare being processed. As discussed above with respect to FIG. 5,following indexing of documents with respect to phrases and relatedphrases. for each document d, there will be known:

i) each good phrase g_(j) in document d;

ii) for each good phrase g_(j) which of its related phrases g_(k) arepresent in document d;

iii) for each related phrase g_(k) present in document d, which of itsrelated phrases g_(l) (the secondary related phrases of g_(j)) are alsopresent in document d.

From the foregoing, the number of the related phrases present in a givendocument will be known. A normal, non-spam document will generally havea relatively limited number of related phrases, typically on the orderof between 8 and 20, depending on the document collection. By contrast,a spam document will have an excessive number of related phrases, forexample on the order of between 100 and 1000 related phrases. Thus, thepresent invention takes advantage of this discovery by identifying asspam documents those documents that have a statistically significantdeviation in the number of related phrases relative to an expectednumber of related phrases for documents in the document collection.

One embodiment of this aspect of the invention is as follows. A table ofspam documents (SPAM_TABLE) is created for storing the document IDs ofthe documents deemed to be spam documents (the table will initially beempty). This is preferably done during the indexing operations describedabove.

The index 150 is traversed with respect to the documents (either all ora significant sample). For each document, there will be a set goodphrases in the document, and for each of these good phrases, there willbe a number of related phrases. An expected number E of related phrasesis determined across the traversed documents, with respect to the goodphrases; the standard deviation of this number is also determined. Inone embodiment the medium (50% percentile) number of related phrases isused as expected number of related phrases in a document.

For each document in the index 150, the actual number N of relatedphrases for each good phrase is determined. Hence, if there are 20phrases in the document, then there will be a vector of 20 values for Nfor the document). This number of related phrases will be the total ofthe bits set in the related phrase bit vectors for each good phrase inthe document. For each phrase then, number N is compared against theexpected number E of related phrases. The results of this comparison,either individually for each good phrase, or collectively for somenumber of good phrases, are used to determine whether the document is aspam document. There a variety of different tests that can be used toidentify a spam document.

A spam document may be indicated if the actual number N of relatedphrases significantly exceeds the expected number E, for some minimumnumber of good phrases. In one implementation, N significantly exceeds Ewhere it is at least some multiple number of standard deviations greaterthan E, for example, more than five standard deviations. In anotherimplementation, N significantly exceeds E where it is greater by someconstant multiple, for example N>2E. Other comparison measures can alsobe used as a basis for determining that the actual number N of relatedphrases significantly exceeds the expected number E. In anotherembodiment, N is simply compared with a predetermined threshold value,such as 100 (which is deemed to be maximum expected number of relatedphrases).

Using any of the foregoing tests, it is determined whether thiscondition is met for some minimum number of good phrases. The minimummay be a single phrase, or perhaps three good phrases. If there are aminimum number of good phrases which have an excessive number of relatedphrases present in the document, then the document is deemed to a spamdocument. The document is then added to the SPAM_TABLE.

Another embodiment maintains a different form of the SPAM_TABLE. In thisembodiment, the table is organized by phrase, and for each phrase, thereis list of one or more documents that include the phrase and which aredeemed to be spam documents. This version of the SPAM_TABLE isconstructed as follows. For each document, the top N (e.g. N=3) mostsignificant phrases are determined. This will be the phrases for whichtheir related phrase bit vectors have the leftmost (most significant)bits set. As described above, the bits in the related phrase bit vectorare sorted by decreasing information gain for the related phrase. Thusthe most significant bits are associated with the related phrases withthe highest information gain.

For each of these most significant related phrases, the number ofrelated phrases present in the document is determined, again from theirrelated phrase bit vectors. If the actual number of related phrasessignificantly exceeds the expected number (using any of the abovedescribed tests), then document is deemed a spam document with respectto that most significant phrase. Accordingly the document is added tothe SPAM_TABLE for the good phrase under consideration. The document isalso added as a spam document for each the related phrases of that goodphrase, since a document is considered a spam document with respect toall phrases that are related to each other.

The foregoing approaches to identifying a spam document are preferablyimplemented as part of the indexing process, and may be conducted inparallel with other indexing operations, are afterwards.

The SPAM_TABLE is then used when processing a search query, as follows.A search query is received from a client 190, and is processed asdescribed above by the search system 120 to search the index 150 basedon phrases in the query and related phrases. The search system 120retrieves some set of results, say a 1000 documents, each of which isidentified by its document ID, and has an associated relevance score.For each document in the search result set, the search system 120 looksup the document ID in the SPAM_TABLE (however constructed), to determineif the document is included therein.

If the document is included in the SPAM_TABLE, then the document'srelevance score is down weighted by predetermined factor. For example,the relevance score can be divided by factor (e.g., 5). Alternatively,the document can simply be removed from the result set entirely.

The search result set is then resorted by relevance score and providedback to the client 190.

The present invention has been described in particular detail withrespect to various embodiments, and those of skill in the art willappreciate that the invention may be practiced in other embodiments. Inaddition, those of skill in the art will appreciate the followingaspects of the disclosure. First, the particular naming of thecomponents, capitalization of terms, the attributes, data structures, orany other programming or structural aspect is not mandatory orsignificant, and the mechanisms that implement the invention or itsfeatures may have different names, formats, or protocols. Second, thesystem may be implemented via a combination of hardware and software, asdescribed, or entirely in hardware elements. Third, the particulardivision of functionality between the various system componentsdescribed herein is merely exemplary, and not mandatory; functionsperformed by a single system component may instead be performed bymultiple components, and functions performed by multiple components mayinstead performed by a single component.

Some portions of above description describe the invention in terms ofalgorithms and symbolic representations of operations on information.These algorithmic descriptions and representations are the means used bythose skilled in the data processing arts to most effectively convey thesubstance of their work to others skilled in the art. These operations,while described functionally, computationally, or logically, areunderstood to be implemented by computer programs or equivalentelectrical circuits, microcode, or the like. Furthermore, it has alsoproven convenient at times, to refer to these arrangements of operationsas modules, without loss of generality. The described operations andtheir associated modules may be embodied in software, firmware orhardware.

In addition, the terms used to describe various quantities, data values,and computations are understood to be associated with the appropriatephysical quantities and are merely convenient labels applied to thesequantities. Unless specifically stated otherwise as apparent from thefollowing discussion, it is appreciated that throughout the description,discussions utilizing terms such as “processing” or “computing” or“calculating” or “determining” or the like, refer to the action andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical(electronic) quantities within the computer system memories or registersor other such information storage, transmission or display devices.

The present invention also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but is not limited to, any type ofdisk including floppy disks, optical disks, CD-ROMs, magnetic-opticaldisks, read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, application specific integratedcircuits (ASICs), or any type of media suitable for storing electronicinstructions, and each coupled to a computer system bus. Furthermore,the computers referred to in the specification may include a singleprocessor or may be architectures employing multiple processor designsfor increased computing capability.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may also be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear from the description above.In addition, the present invention is not described with reference toany particular programming language. It is appreciated that a variety ofprogramming languages may be used to implement the teachings of thepresent invention as described herein, and any references to specificlanguages are provided for disclosure of enablement and best mode of thepresent invention.

The present invention is well-suited to a wide variety of computernetwork systems over numerous topologies. Within this field, theconfiguration and management of large networks comprise storage devicesand computers that are communicatively coupled to dissimilar computersand storage devices over a network, such as the Internet.

Finally, it should be noted that the language used in the specificationhas been principally selected for readability and instructionalpurposes, and may not have been selected to delineate or circumscribethe inventive subject matter. Accordingly, the disclosure of the presentinvention is intended to be illustrative, but not limiting, of the scopeof the invention, which is set forth in the following claims.

1. A computer implemented method for identifying spam documents in aninformation retrieval system, the method comprising: maintaining a listof phrases, each phrase associated with a list of related phrases;determining a number of related phrases expected to be present in adocument for any phrase on the list of phrases; determining for adocument, and for at least one phrase in the document, an actual numberof related phrases present in the document; and identifying the documentas a spam document by comparing the actual number of related phrasespresent in the document with the expected number of related phrases. 2.The method of claim 1, wherein determining a number of related phrasesexpected to be present in a document for any phrase on the list ofphrases further comprises: traversing an index of documents; for eachdocument, determining a set of phrases in the document from the list ofphrases, and for each phrase in the document, determining a number ofrelated phrases also in the document; determining the expected number ofrelated phrases, as a medium of the determined number of related phrasesacross the traversed documents.
 3. The method of claim 1, whereinidentifying the document as a spam document, further comprises:responsive to the actual number of related phrases present in thedocument for at least one phrase significantly exceeding the expectednumber of related phrases, identifying the document as a spam document.4. The method of claim 1, wherein identifying the document as a spamdocument, further comprises: responsive to the actual number of relatedphrases present in the document for at least one phrase exceeding theexpected number of related phrases by at least a multiple of a standarddeviation of the expected number of related phrases, identifying thedocument as a spam document.
 5. The method of claim 1, whereinidentifying the document as a spam document, further comprises:responsive to the actual number of related phrases present in thedocument for at least one phrase exceeding the expected number ofrelated phrases by at least a multiple of the expected number of relatedphrases, identifying the document as a spam document.
 6. The method ofclaim 1, wherein identifying the document as a spam document, furthercomprises: identifying the document as a spam document where, for eachof a minimum plurality of phrases in the document, the actual number ofrelated phrases present in the document significantly exceeds theexpected number of related phrases.
 7. The method of claim 1, whereinidentifying the document as a spam document, further comprises:identifying the document as a spam document where the actual number ofrelated phrases present in the document for at least one phrase exceedspredetermined maximum expected number of related phrases.
 8. The methodof claim 1, wherein identifying the document as a spam document, furthercomprises: determining for a document, a set of most significant phrasespresent in the document; for each of the most significant relatedphrases, determining an actual number of related phrases present in thedocument; and responsive to the actual number of related phrasessignificantly exceeds the expected number of related phrases,identifying the document as a spam document with respect to thatsignificant phrase.
 9. The method of claim 1, further comprises:responsive to identifying the document as a spam document, adding thedocument to a list of spam documents.
 10. The method of claim 9, furthercomprising: receiving a search query; retrieving a set of documentsrelevant to the search query, each document having a relevance score;for each document in the set of documents, determining whether thedocument has been identified as a spam document; and responsive to adocument being identified as a spam document, down weighting therelevance score of the document; organizing the set of documents bytheir relevance scores.
 11. The method of claim 8, further comprising:adding the document to a list of spam document associated with the mostsignificant phrase; and for each related phrase of the most significantphrase, adding the document to a list of spam documents associated withthe related phrase.